Typical Load Profiles Definitions for Industrial Facilities Based on Energy Consumption Data Clustering
This study investigates the application of machine learning clustering techniques, specifically Dynamic Time Warping (DTW), to define typical load profiles (TLPs) for industrial facilities. Utilizing $\mathbf{1 5}$-minute smart meter data from a plastics manufacturing plant, the research analyzes total factory consumption alongside individual chiller and compressor loads. Cluster quality is assessed using the Silhouette score, Dunn index, and mean intra-cluster distance. Results indicate that while DTW effectively captures temporal shapes, industrial profiles are highly enterprise-specific and noise-intensive, resulting in fair-to-weak cluster quality. The findings suggest that primary electricity datasets and basic temporal metadata are insufficient for high-quality profiling compared to existing household models. The study concludes that integrating production-related metadata, such as work orders, is essential for improving industrial consumption forecasting and capacity planning.